


import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute






def 				__lowercase  (					__lowerCAmelCase				:							str = 3  ):
							if isinstance(__lowerCAmelCase   ,       __lowerCAmelCase  ):
														raise TypeError('number of qubits must be a integer.'  )
							if number_of_qubits <= 0:
														raise ValueError('number of qubits must be > 0.'  )
							if math.floor(__lowerCAmelCase  ) != number_of_qubits:
														raise ValueError('number of qubits must be exact integer.'  )
							if number_of_qubits > 1_0:
														raise ValueError('number of qubits too large to simulate(>10).'  )

							a__			 =	QuantumRegister(__lowerCAmelCase   ,       'qr'  )
							a__			 =	ClassicalRegister(__lowerCAmelCase   ,       'cr'  )

							a__			 =	QuantumCircuit(__lowerCAmelCase   ,       __lowerCAmelCase  )

							a__			 =	number_of_qubits

							for i in range(__lowerCAmelCase  ):
														quantum_circuit.h(number_of_qubits - i - 1  )
														counter -= 1
														for j in range(__lowerCAmelCase  ):
																					quantum_circuit.cp(np.pi / 2 ** (counter - j)   ,       __lowerCAmelCase   ,       __lowerCAmelCase  )

							for k in range(number_of_qubits // 2  ):
														quantum_circuit.swap(__lowerCAmelCase   ,       number_of_qubits - k - 1  )

							# measure all the qubits
							quantum_circuit.measure(__lowerCAmelCase   ,       __lowerCAmelCase  )
							# simulate with 10000 shots
							a__			 =	Aer.get_backend('qasm_simulator'  )
							a__			 =	execute(__lowerCAmelCase   ,       __lowerCAmelCase   ,       shots=1_0_0_0_0  )

							return job.result().get_counts(__lowerCAmelCase  )


if __name__ == "__main__":
						print(
						    f"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
						)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






